A high-performance, low-energy FPGA accelerator for correntropy-based feature tracking (abstract only)

Computer-vision and signal-processing applications often require feature tracking to identify and track the motion of different objects (features) across a sequence of images. Numerous algorithms have been proposed, but common measures of similarity for real-time usage are either based on correlation, mean-squared error, or sum of absolute differences, which are not robust enough for safety-critical applications. To improve robustness, a recent feature-tracking algorithm called C-Flow uses correntropy from Information Theoretic Learning to significantly improve signal-to-noise ratio. In this paper, we present an FPGA accelerator for C-Flow that is typically 3.6-8.5x faster than a GPU and show that the FPGA is the only device capable of real-time usage for large features. Furthermore, we show the FPGA accelerator is more appropriate for embedded usage, with energy consumption that is 2.5-22x less than the GPU.